--- title: "Social Sentiment Analysis with Hadoop" description: "Social sentiment analysis is simply the most overrated of the Hadoop applications, which should be no surprise, given that we breathe in a world with" author: "Anonymous User" published: 2016-04-29 updated: 2018-03-14 canonical: https://www.mindstick.com/blog/11045/social-sentiment-analysis-with-hadoop category: "big data" tags: ["bigdata", "hadoop", "software development"] reading_time: 3 minutes --- # Social Sentiment Analysis with Hadoop Social [sentiment analysis](https://www.mindstick.com/articles/156985/sentiment-analysis-using-python-in-tableau-with-tabpy) is simply the most overrated of the Hadoop applications, which should be no surprise, given that we breathe in a world with a constantly connected and expressive population. This use of Hadoop leverages all kind of contents from [content management](https://www.mindstick.com/articles/327061/10-tips-to-improve-your-business-content-management-in-2021) systems, **blogs, forums** and other social media tools to generate a sense of what individuals are doing (for instance, life events) and how they react to the people around them (sentiment). Since text-based data doesn’t usually fit into a [relational database](https://www.mindstick.com/forum/158346/what-is-sql-server-and-how-does-it-differ-from-other-relational-database-management-systems) (RDMS’s), Hadoop is a perfect destination to explore and analyse this kind of data. Language is difficult to interpret, even for [human beings](https://yourviews.mindstick.com/story/2204/7-unknown-powerful-bodies-of-human-beings) at times — especially if we are reading text written by people in a social group that’s different from our own. This [group of people](https://answers.mindstick.com/qa/51236/helen-hunt-jackson-s-a-century-of-dishonor-chronicled-the-injustices-inflicted-upon-which-group-of-people) may be speaking our language, but their expressions and style are completely foreign, so we have no idea whether they’re talking about a good experience or a bad one. For example, if we heard the phrase bomb with reference to a movie, we may conclude that the movie was not good (or good, if we are part of the youth movement that recognizes “its bomb” as a compliment); also, if we are in the airline security business, this phrase bomb would led us to a different interpretation. The thing is that linguistics is used in variety of distinct ways and is constantly evolving. When we analyse sentiment on [social media](https://www.mindstick.com/articles/13056/how-to-leverage-social-media-to-attract-lots-of-blog-traffic), we can choose from multiple approaches. The basic method programmatically parses the text phrases and expressions, extracts strings, and applies logics or rules. **In most common conditions,** this mechanism is practical and reasonable. But as a requirement varies and rules get more complicated, manually coding text-extractions clearly becomes no longer effectively feasible from the point of view for code maintenance, especially for [performance optimization](https://www.mindstick.com/forum/160291/explain-the-importance-of-proper-indexing-in-stored-procedures-for-performance-optimization). Grammar- and rules-based strategies to text processing are computationally expensive, which is an important constraint in large-scale extraction in Hadoop. The greater involved the rules (which are inevitable for complex purposes such as sentiment extraction), the more processing that’s needed. In [software development](https://www.mindstick.com/services/erp-software-development), a statistics-based alternative approach is becoming increasingly common for sentiment analysis. Rather than manually write complex rules, we can use the classification-oriented machine-[learning models](https://www.mindstick.com/forum/160783/what-are-the-main-differences-between-generative-ai-and-traditional-machine-learning-models) in Apache Mahout. The catch here is that we will need to train our models with examples of positive and negative sentiment. The more [training data](https://answers.mindstick.com/qa/116715/explain-the-importance-of-training-data-and-testing-data) we provide (for example, text from tweets and your classification), the more accurate our results. The social sentiment analysis can be applied across a wide range of industries for example, food safety, health care etc. --- Original Source: https://www.mindstick.com/blog/11045/social-sentiment-analysis-with-hadoop Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.